Where Data Meets Human Behavior at SocioLogic
Dr. Sarah Chen spent a decade in academia studying how people make decisions before realizing that the real-world applications of her research were being held back by traditional research methods.
After completing her PhD in Behavioral Economics, she led research teams at two Fortune 500 companies where she became increasingly frustrated with the limitations of conventional market research: small sample sizes, slow turnaround times, and the impossibility of asking follow-up questions after a study concluded.
When she discovered the potential of AI-powered synthetic personas, she saw an opportunity to revolutionize how companies understand their customers. At SocioLogic, she leads our research methodology, ensuring that our synthetic users behave in ways that are grounded in actual human psychology.
Sarah's writing tends toward the technical and evidence-based. She frequently references academic studies and isn't afraid to dive deep into methodology. Her articles are favorites among research professionals and data scientists who appreciate her rigorous approach.
When she's not nerding out about research methodology, Sarah enjoys trail running in the Berkeley hills and has an inexplicable obsession with collecting vintage research equipment—her office features a 1960s tachistoscope that she insists still works.
How vector embeddings and map-reduce create traceable, defensible research
We anchor simulations using RAG vector embeddings and map-reduce architecture. Here's how this grounds every insight in real-world data rather than creative probability.
How we engineered 100% token retention for forensic-grade simulation
Traditional RAG pipelines suffer from context loss. Here's how SocioLogic's map-reduce architecture ensures every finding is traceable and defensible across 50-cohort simulations.